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| import streamlit as st | |
| from PIL import Image | |
| import os | |
| import pandas as pd | |
| import tomli | |
| pd.options.mode.chained_assignment = None | |
| st.set_page_config(layout="wide") | |
| st.markdown( | |
| "<div style='background-color: lightblue; text-align: center; padding: 10px;'><h1 style='font-size: 70px;'>Our History in Data</h1></div>", | |
| unsafe_allow_html=True, | |
| ) | |
| def load_data(): | |
| df_ind = pd.read_csv("data/df_individuals_score.csv", index_col=[0]) | |
| df_ind["productive_year"] = df_ind["productive_year"].astype(int) | |
| df_ind["individual_wikidata_id"] = "https://www.wikidata.org/wiki/" + df_ind[ | |
| "individual_wikidata_id" | |
| ].astype(str) | |
| df_ind = df_ind[df_ind["productive_year"] <= 1800] | |
| return df_ind | |
| df_ind = load_data() | |
| def load_region_descriptions(): | |
| with open("regions.toml", "rb") as toml_file: | |
| data = tomli.load(toml_file) | |
| return data | |
| # Function to get description based on selected region | |
| def get_region_description(region_data, selected_region): | |
| return region_data[selected_region]["description"] | |
| region_data = load_region_descriptions() | |
| # page = st.sidebar.selectbox("Navigate to:", ["Home", "Methodology", "Team"]) | |
| page = st.sidebar.radio( | |
| "Menu", | |
| ["Home", "Methodology", "Team", "About"], | |
| key="navigation_radio", | |
| ) | |
| st.sidebar.success( | |
| "This project is led by Charles de Dampierre, Folgert Karsdorp, Mike Kestemont, Valentin Thouzeau and Nicolas Baumard" | |
| ) | |
| # Test change | |
| if page == "Home": | |
| # Set the global index path | |
| global_index_path = "data/immaterial_index/figures_trends_R/results" | |
| global_index_path_per_capita = ( | |
| "data/immaterial_index/figures_trends_R/results_capita" | |
| ) | |
| unseen_index_path = "data/immaterial_index/figures_trends_R/results_unseen" | |
| unseen_capita_index_path = ( | |
| "data/immaterial_index/figures_trends_R/results_unseen_capita" | |
| ) | |
| complexity_index_path = "data/immaterial_index/figures_trends_R/results_complexity" | |
| occupations_index_path = ( | |
| "data/immaterial_index/figures_trends_R/results_occupations" | |
| ) | |
| population_path = "data/population" | |
| gdp_per_capita_path = "data/gdp_per_capita" | |
| maps_path = "data/map_figures" | |
| from region_list import region_list | |
| region_filtered = list(region_list.keys()) | |
| index_paths = {} | |
| for region_key in region_list: | |
| # Create the index paths for the current region | |
| index_paths[region_key] = { | |
| "map": f"{maps_path}/map_{region_key}.png", | |
| "global_index": f"{global_index_path}/{region_key}.png", | |
| "global_index_per_capita": f"{global_index_path_per_capita}/{region_key}.png", | |
| "unseen_index": f"{unseen_index_path}/{region_key}.png", | |
| "unseen_index_capita": f"{unseen_capita_index_path}/{region_key}.png", | |
| "complexity_index": f"{complexity_index_path}/{region_key}.png", | |
| "occupations_index": f"{occupations_index_path}/{region_key}.png", | |
| "gdp_per_capita_index": f"{gdp_per_capita_path}/{region_key}.png", | |
| "population_index": f"{population_path}/{region_key}.png", | |
| } | |
| # Get the region names (keys) from the index_paths dictionary | |
| regions = list(index_paths.keys()) | |
| # Allow the user to select a region | |
| selected_region = st.sidebar.selectbox( | |
| "Region:", regions, index=regions.index("Japan") | |
| ) | |
| # Display the selected region's images vertically | |
| if selected_region in index_paths: | |
| st.markdown( | |
| f"<h1 style='text-align: left; font-size: 50px;'>{selected_region}</h1>", | |
| unsafe_allow_html=True, | |
| ) | |
| try: | |
| st.image( | |
| f"image/{selected_region}.jpeg", | |
| caption="Japan", | |
| use_column_width=False, | |
| width=1000, | |
| ) | |
| except: | |
| pass | |
| col1, col2, col3 = st.columns([8, 1, 8]) | |
| # Display the data in the left column | |
| with col1: | |
| for key, path in index_paths[selected_region].items(): | |
| if os.path.exists(path): | |
| if key == "global_index": | |
| st.subheader("Cultural Index") | |
| st.image( | |
| Image.open(path), | |
| caption=key.capitalize(), | |
| use_column_width=True, | |
| ) | |
| elif key == "global_index_per_capita": | |
| st.subheader("Cultural Index per capita") | |
| st.image( | |
| Image.open(path), | |
| caption=key.capitalize(), | |
| use_column_width=True, | |
| ) | |
| elif key == "unseen_index": | |
| st.subheader( | |
| "Cultural Index corrected by the unseen-species model" | |
| ) | |
| st.image( | |
| Image.open(path), | |
| caption=key.capitalize(), | |
| use_column_width=True, | |
| ) | |
| elif key == "unseen_index_capita": | |
| st.subheader( | |
| "Cultural Index per capita corrected by the unseen-species model" | |
| ) | |
| st.image( | |
| Image.open(path), | |
| caption=key.capitalize(), | |
| use_column_width=True, | |
| ) | |
| # elif key == "complexity_index": | |
| # st.subheader("Complexity Index") | |
| # st.image( | |
| # Image.open(path), | |
| # caption=key.capitalize(), | |
| # use_column_width=True, | |
| # ) | |
| elif key == "occupations_index": | |
| st.subheader("Occupation Index") | |
| st.image( | |
| Image.open(path), | |
| caption=key.capitalize(), | |
| use_column_width=True, | |
| ) | |
| elif key == "gdp_per_capita_index": | |
| st.subheader("GDP per capita") | |
| st.image( | |
| Image.open(path), | |
| caption=key.capitalize(), | |
| use_column_width=True, | |
| ) | |
| else: | |
| st.write(f"File for {key.capitalize()} does not exist.") | |
| with col3: | |
| try: | |
| st.image( | |
| Image.open(f"data/map_figures/map_{selected_region}.png"), | |
| use_column_width=True, | |
| width=1000, | |
| ) | |
| except: | |
| pass | |
| st.subheader("Analysis") | |
| try: | |
| region_description = get_region_description( | |
| region_data, selected_region | |
| ) | |
| st.write(f"{region_description}") | |
| except: | |
| st.write("Analysis not ready yet") | |
| st.subheader("Cultural Producers in Wikidata") | |
| df = df_ind[df_ind["region_name"] == selected_region] | |
| df = df.drop(["region_name", "decade"], axis=1) | |
| df = df[ | |
| [ | |
| "individual_name", | |
| "productive_year", | |
| "score", | |
| "individual_wikidata_id" "", | |
| ] | |
| ] | |
| df = df.sort_values("score", ascending=False) | |
| df = df.rename(columns={"score": "Number of Catalogs"}) | |
| min_date = region_list[selected_region]["time_range"][0] | |
| max_date = region_list[selected_region]["time_range"][1] | |
| df = df[df["productive_year"] >= min_date] | |
| df = df[df["productive_year"] <= max_date] | |
| # df["productive_year"] = df["productive_year"] * 1000 | |
| df["productive_year"] = round(df["productive_year"], 0).astype(str) | |
| # df["productive_year"] = round(df["productive_year"], 0).astype(int) | |
| df = df.reset_index(drop=True) | |
| st.dataframe(df) | |
| st.write(f"Number of Cultural producers active before 1800: {len(df)}") | |
| try: | |
| st.subheader("Population") | |
| st.image( | |
| Image.open(path), | |
| caption=key.capitalize(), | |
| use_column_width=True, | |
| ) | |
| except: | |
| pass | |
| elif page == "Methodology": | |
| # Read the content of the file methodolog.md | |
| with open("docs/methodology.md", "r") as file: | |
| methodology_content = file.read() | |
| # Display the content in the Streamlit app | |
| st.write(methodology_content) | |